computeRSquared {AffiXcan} | R Documentation

## Compute R and R^2 between rows of two SummarizedExperiment assays

### Description

Compute R and R^2 between rows of two SummarizedExperiment assays

### Usage

    computeRSquared(
      realExpr,
      imputedExpr,
      assay,
      testingSamples = NULL,
      BPPARAM = bpparam()
    )

### Arguments

`realExpr` | A SummarizedExperiment object containing expression data
`imputedExpr` | The returning object of affiXcanImpute()
`assay` | A string with the name of the object in SummarizedExperiment::assays(realExpr) that contains expression values
`testingSamples` | A vector of strings. The identifiers of the samples that have to be considered by the function; default is NULL; if is.null(testingSamples)==TRUE then no filtering is performed
`BPPARAM` | A BiocParallelParam object. Default is bpparam(). For details on BiocParallelParam virtual base class see browseVignettes("BiocParallel")

### Value

A list of lists; inner lists are named after the rows for which the correlation between realExpr and imputedExpr have been computed; inner lists contain two objects:

- rho: the pearson's correlation coefficient (R) between the real expression values and the imputed GReX for the cross-validation i on the expressed gene y, computed with cor()

- rho.sq: the coefficient of determination (R^2) between the real expression values and the imputed GReX for the cross-validation i on the expressed gene y, computed as pearson^2

- cor.test.p.val: the p-value of the cor.test() between the real expression values and the imputed GReX for the cross-validation i on the expressed gene y

### Examples

    if (interactive()) {
    trainingTbaPaths <- system.file("extdata","training.tba.toydata.rds",
    package="AffiXcan")

    data(exprMatrix)
    data(regionAssoc)
    data(trainingCovariates)

    assay <- "values"

    training <- affiXcanTrain(exprMatrix=exprMatrix, assay=assay,
    tbaPaths=trainingTbaPaths, regionAssoc=regionAssoc, cov=trainingCovariates,
    varExplained=80, scale=TRUE)

    imputedExpr <- affiXcanImpute(tbaPaths=trainingTbaPaths,
    affiXcanTraining=training, scale=TRUE)
    realExpr <- exprMatrix

    correlation <- computeRSquared(realExpr, imputedExpr, assay)
    }

[Package AffiXcan version 1.10.0 Index]